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x# Import libraries and dependenciesimport osimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport alpaca_trade_api as tradeapifrom MCForecastTools import MCSimulation​%matplotlib inline

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-1-5d57e9b5bad8> in <module>
      4 import pandas as pd
      5 import matplotlib.pyplot as plt
----> 6 import alpaca_trade_api as tradeapi
      7 from MCForecastTools import MCSimulation
      8 

ModuleNotFoundError: No module named 'alpaca_trade_api'

 # Load .env enviroment variablesfrom dotenv import load_dotenvload_dotenv()​# Set Alpaca API key and secretalpaca_api_key = os.getenv("ALPACA_API_KEY")alpaca_secret_key = os.getenv("ALPACA_SECRET_KEY")​api = tradeapi.REST(    alpaca_api_key,    alpaca_secret_key,    api_version = "v2")

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 # Set the tickerticker = "TSLA"​# Set timeframe to '1D'timeframe = "1D"​# Set start and end datetimes of 3 years from Todaystart_date = pd.Timestamp("2017-05-04", tz="America/New_York").isoformat()end_date = pd.Timestamp("2020-05-04", tz="America/New_York").isoformat()​# Get 3 years worth of historical data for TSLAticker_data = api.get_barset(    ticker,    timeframe,    start=start_date,    end=end_date,    limit=1000,).df​ticker_data.head()

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 # Set number of simulationsnum_sims = 1000​# Configure a Monte Carlo simulation to forecast three years daily returnsMC_TSLA = MCSimulation(    portfolio_data = ticker_data,    num_simulation = num_sims,    num_trading_days = 252*3)

 # Run Monte Carlo simulations to forecast three years daily returnsMC_TSLA.calc_cumulative_return()

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 # Compute summary statistics from the simulated daily returnssimulated_returns_data = {    "mean": list(MC_TSLA.simulated_return.mean(axis=1)),    "median": list(MC_TSLA.simulated_return.median(axis=1)),    "min": list(MC_TSLA.simulated_return.min(axis=1)),    "max": list(MC_TSLA.simulated_return.max(axis=1))}​# Create a DataFrame with the summary statisticsdf_simulated_returns = pd.DataFrame(simulated_returns_data)​# Display sample datadf_simulated_returns.head()

 # Use the `plot` function to visually analyze the trajectory of TSLA stock daily returns on the next three years of trading days simulationdf_simulated_returns.plot(title="Simulated Daily Returns Behavior of TSLA Stock Over the Next Year")

<matplotlib.axes._subplots.AxesSubplot at 0x7faaf8c88610>

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 # Set initial investmentinitial_investment = 10000​# Multiply an initial investment by the daily returns of simulative stock prices to return the progression of daily returns in terms of moneycumulative_pnl = initial_investment * df_simulated_returns​# Display sample datacumulative_pnl.head()

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 # Use the 'plot' function to create a chart of the simulated profits/lossescumulative_pnl.plot(title="Simulated Outcomes Behavior of TSLA Stock Over the Next Year")

<matplotlib.axes._subplots.AxesSubplot at 0x7faaf2e62190>

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 # Fetch summary statistics from the Monte Carlo simulation resultstbl = MC_TSLA.summarize_cumulative_return()​# Print summary statisticsprint(tbl)

count           1000.000000
mean               4.906131
std                6.899562
min                0.126797
25%                1.232316
50%                2.655507
75%                6.011975
max               85.991793
95% CI Lower       0.371666
95% CI Upper      23.772773
Name: 756, dtype: float64

 # Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $10,000 investments in TSLA stocksci_lower = round(tbl[8]*10000,2)ci_upper = round(tbl[9]*10000,2)​# Print resultsprint(f"There is a 95% chance that an initial investment of $10,000 in the portfolio"      f" over the next year will end within in the range of"      f" ${ci_lower} and ${ci_upper}.")

There is a 95% chance that an initial investment of $10,000 in the portfolio over the next year will end within in the range of $3716.66 and $237727.73.

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 # Import dependenciesimport pandas as pd

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 # Create reference to CSV file# Hint - the file path may require some extra steps..csv_path = "../Resources/world_happiness.csv"

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 # Import the CSV into a pandas DataFramehappiness_df = pd.read_csv(csv_path, low_memory=False)happiness_df

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 # Set new index to Countryhappiness_df = happiness_df.set_index("Country")​# Sort the new dataframe alphabeticallyhappiness_df.sort_index(inplace=True)happiness_df.head(30)

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 # Grab the data contained within the row of a country of your choice and the "Happiness.Score" column# Print the result as "<country's name> happiness score for 2017 was <happiness score>"happy_score = happiness_df.loc["Vietnam", "Happiness.Score"]print("Vietnam's happiness score for 2017 was " + str(happy_score))

Vietnam's happiness score for 2017 was 5.07399988174438

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 # Grab all rows of data from "Belgium" to "Bolivia" and the columns from "Happiness.Rank" to "Happiness.Score"# Hint - in order to view more data you will need to increase the dataframe's headbelgium_to_bolivia = happiness_df.loc[["Belgium", "Belize", "Benin",                                "Bhutan", "Bolivia"], ["Happiness.Rank", "Happiness.Score"]]belgium_to_bolivia

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 # Select all rows and only display the columns for "Freedom" and "Trust..Government.Corruption." happiness_df.loc[:, ["Freedom", "Trust..Government.Corruption."]].head()

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 # Select all rows where the GDP per capita has a value greater than or equal to 1.6gdp_score = happiness_df.loc[happiness_df["Economy..GDP.per.Capita."] >= 1.6, :]gdp_score.head()

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 # Select all rows that have a happiness score greater than or equal 5 and a GDP score less than or equal 1happy_but_poor_score = happiness_df.loc[(happiness_df["Happiness.Score"] >= 5) &                                         (happiness_df["Economy..GDP.per.Capita."] <= 1), :]happy_but_poor_score.head()

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 import pandas as pdimport hvplot.pandasfrom pathlib import Path

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 # Read in loan dataloan_data = pd.read_csv(Path("../Resources/state_loan_data.csv"))​# Group data by state for state-level analysisloan_data_state = loan_data.groupby('State Code').sum()loan_data_state.head()

 loan_data_state.head()

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 # Slice for Total Average Loan Amountloan_data_state = loan_data['Total Average Loan Amount']loan_data_avg_grp = loan_data_state.sort_values()loan_data_avg_grp.index.names = ['State Code 0']​# Plot Total Average Loan Amountplot_state_avgs = loan_data_avg_grp.hvplot.bar(label='2019 Total Average Amount')plot_state_avgs

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 # Slice data for Total Average Loan Amount by 2015-2016 and 2010-2014 date rangesloan_data_range_1 = loan_data['2015 - 2016']loan_data_range_2 = loan_data['2010 - 2014']loan_data_range_grp = loan_data_range_1.sort_values()loan_data_range_grp_2 = loan_data_range_2.sort_values()# Recall, that the + operator aligns the index of both dataframes by default. We prevent this by renaming the indexesloan_data_range_grp.index.names = ['State Code 1']loan_data_range_grp_2.index.names = ['State Code 2']​# Plot data for date rangesplot_2015_2016 = loan_data_range_grp.hvplot(label='2015 - 2016') plot_2010_2014 = loan_data_range_grp_2.hvplot(label='2010 - 2014')​

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 # Compose plotsplot_2015_2016 + plot_2010_2014

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 # Compose plotsplot_state_avgs + plot_2015_2016 + plot_2010_2014

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 loan_data['Total Average Loan Amount'].hvplot.bar(label='2019 Total Average Amount') *  loan_data['2015 - 2016'].hvplot(label='2015 - 2016')  * loan_data['2010 - 2014'].hvplot(label='2010 - 2014')

​

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ximport plotly.express as pximport pandas as pdfrom pathlib import Path

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 # Read in dataforeclosures = pd.read_csv(    Path("../../Resources/alleghany_foreclosures.csv"),    infer_datetime_format=True,    parse_dates=True,    index_col="filing_date",)​# Slice data and groupforeclosures_grp = (    foreclosures[["municipality", "amount"]]    .groupby([foreclosures.index.year, "municipality"])    .count()    .reset_index())foreclosures_grp.head()

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 # Create scatter plotpx.scatter(foreclosures_grp, x="municipality", y="amount", color="filing_date")

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 # Create scatter plotpx.scatter(foreclosures_grp, x="municipality", y="filing_date", size="amount")

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 # Create area plotpx.area(    foreclosures_grp,    x="filing_date",    y="amount",    color="municipality",    line_group="municipality",)

​

​

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 # Import libraries and dependenciesimport osimport pandas as pdimport alpaca_trade_api as tradeapifrom MCForecastTools import MCSimulation

 # Load .env enviroment variablesfrom dotenv import load_dotenvload_dotenv()

True

 # Set Alpaca API key and secretalpaca_api_key = os.getenv("ALPACA_API_KEY")alpaca_secret_key = os.getenv("ALPACA_SECRET_KEY")​api = tradeapi.REST(    alpaca_api_key,    alpaca_secret_key,    api_version = "v2")

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 # Set timeframe to '1D'timeframe = "1D"​# Set start and end datetimes between now and 3 years ago.start_date = pd.Timestamp("2017-05-01", tz="America/New_York").isoformat()end_date = pd.Timestamp("2020-05-01", tz="America/New_York").isoformat()​# Set the ticker informationtickers = ["MSFT","KO"]​# Get 3 year's worth of historical price data for Microsoft and Coca-Coladf_ticker = api.get_barset(    tickers,    timeframe,    start=start_date,    end=end_date,    limit=1000,).df​# Display sample datadf_ticker.head()

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 # Print the documentation of the MCSimulation module of the MCForecastTools library?MCSimulation

Init signature:
MCSimulation(
    portfolio_data,
    weights='',
    num_simulation=1000,
    num_trading_days=252,
)
Docstring:     
A Python class for runnning Monte Carlo simulation on portfolio price data. 

...

Attributes
----------
portfolio_data : pandas.DataFrame
    portfolio dataframe
weights: list(float)
    portfolio investment breakdown
nSim: int
    number of samples in simulation
nTrading: int
    number of trading days to simulate
simulated_return : pandas.DataFrame
    Simulated data from Monte Carlo
confidence_interval : pandas.Series
    the 95% confidence intervals for simulated final cumulative returns
    
Init docstring:
Constructs all the necessary attributes for the MCSimulation object.

Parameters
----------
portfolio_data: pandas.DataFrame
    DataFrame containing stock price information from Alpaca API
weights: list(float)
    A list fractions representing percentage of total investment per stock. DEFAULT: Equal distribution
num_simulation: int
    Number of simulation samples. DEFAULT: 1000 simulation samples
num_trading_days: int
    Number of trading days to simulate. DEFAULT: 252 days (1 year of business days)
File:           ~/Code/FinTech-Lesson-Plans/01-Lesson-Plans/05-APIs/3/Activities/03-Ins_Portfolio_Forecasting_Monte_Carlo/Solved/MCForecastTools.py
Type:           type
Subclasses:

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 # Configuring a Monte Carlo simulation to forecast five years cumulative returnsMC_fiveyear = MCSimulation(    portfolio_data = df_ticker,    weights = [.60,.40],    num_simulation = 500,    num_trading_days = 252*5))

 # Printing the simulation input dataMC_fiveyear.portfolio_data.head()

 # Running a Monte Carlo simulation to forecast five years cumulative returnsMC_fiveyear.calc_cumulative_return()

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 # Plot simulation outcomesline_plot = MC_fiveyear.plot_simulation()​# Save the plot for future usageline_plot.get_figure().savefig("MC_fiveyear_sim_plot.png", bbox_inches="tight")

 # Plot probability distribution and confidence intervalsdist_plot = MC_fiveyear.plot_distribution()​# Save the plot for future usagedist_plot.get_figure().savefig('MC_fiveyear_dist_plot.png',bbox_inches='tight')

 # Fetch summary statistics from the Monte Carlo simulation resultstbl = MC_fiveyear.summarize_cumulative_return()​# Print summary statisticsprint(tbl)

count           500.000000
mean              2.325262
std               0.990804
min               0.775205
25%               1.606110
50%               2.129262
75%               2.787852
max               6.779987
95% CI Lower      0.987389
95% CI Upper      4.934141
Name: 1260, dtype: float64

 # Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $10,000 investments in Coca-Cola and Microsoft stocksci_lower = round(tbl[8]*10000,2)ci_upper = round(tbl[9]*10000,2)​# Print resultsprint(f"There is a 95% chance that an initial investment of $10,000 in the portfolio"      f" over the next 5 years will end within in the range of"      f" ${ci_lower} and ${ci_upper}")

There is a 95% chance that an initial investment of $10,000 in the portfolio over the next 5 years will end within in the range of $9873.89 and $49341.41

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 import pandas as pdfrom pathlib import Path

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 # Read csv datacsv_path = Path("../../Resources/goog_google_finance.csv")goog_df = pd.read_csv(csv_path, parse_dates=True, index_col="Date", infer_datetime_format=True)goog_df.head()

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 # Check for nullsgoog_df.isnull().mean() * 100​# Drop nullsgoog_df = goog_df.dropna()​# Drop duplicatesgoog_df = goog_df.drop_duplicates()​# Validate no more missing valuesgoog_df.isnull().sum()

Close    0
dtype: int64

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 # Set multi-index by groupinggoog_df_grp = goog_df.groupby([goog_df.index.year, goog_df.index.month]).first()goog_df_grp.head()

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 # Select GOOG Close for May 2019google_may_2019_data = goog_df_grp.loc[2019, 5]google_may_2019_data

Close    1132.03
Name: (2019, 5), dtype: float64

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 goog_mean_may_2019 = goog_df_grp.loc[2019].mean()goog_mean_may_2019

Close    1153.435
dtype: float64

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 import pandas as pdimport numpy as npfrom pathlib import Path%matplotlib inline

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 harold_portfolio_path = Path("../../Resources/harold_portfolio.csv")my_portfolio_path = Path("../../Resources/my_portfolio.csv")​# Read in dataharold_portfolio = pd.read_csv(harold_portfolio_path, parse_dates=True, infer_datetime_format=True)my_portfolio = pd.read_csv(my_portfolio_path, parse_dates=True, infer_datetime_format=True)​# Drop naharold_portfolio = harold_portfolio.dropna()my_portfolio = my_portfolio.dropna()​# Set Date as indexharold_portfolio = harold_portfolio.set_index('Date')my_portfolio = my_portfolio.set_index('Date')​harold_portfolio.head()

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 # Calculate daily returns for each portfolioharold_returns = harold_portfolio.pct_change().dropna()my_returns = my_portfolio.pct_change().dropna()harold_returns.head()

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 # Concat returns DataFramesall_returns = pd.concat([harold_returns,my_returns], axis='columns', join='inner')all_returns.head()

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 # Calculate std devall_portfolio_std = all_returns.std()all_portfolio_std.head()

BTC     0.070488
BTT     0.030731
DOGE    0.050907
ETH     0.091517
LTC     0.084703
dtype: float64

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 # Calculate sharpe ratiosharpe_ratios = (all_returns.mean() * 252) / (all_portfolio_std * np.sqrt(252))sharpe_ratios.head()

BTC      0.936891
BTT    -32.935733
DOGE     5.615811
ETH     -5.638309
LTC     -0.796855
dtype: float64

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 # Plotsharpe_ratios.plot.bar(title='Sharpe Ratios')

<matplotlib.axes._subplots.AxesSubplot at 0x11c4abe10>

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 # Calculate std dev for all investments for each portfolioharold_std_annual = harold_returns.std() * np.sqrt(252)my_std_annual = my_returns.std() * np.sqrt(252)​harold_std_annual

BTC      1.118962
BTT      0.487842
DOGE     0.808117
ETH      1.452783
LTC      1.344613
XRP      1.274365
SOLVE    0.936847
XLM      1.044947
TRON     1.030614
ZIL      1.027496
dtype: float64

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 # Calculate sharpe ratioharold_sharpe_ratios = (harold_returns.mean() * 252) / (harold_std_annual)my_sharpe_ratios = (my_returns.mean() * 252) / (my_std_annual)​my_sharpe_ratios.head()

BTC    -0.666636
DOGE   -0.020114
ETH    -0.389330
LTC    -0.285959
XRP    -0.212338
dtype: float64

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 # Averaging sharpe ratiosharold_sharpe_avg = harold_sharpe_ratios.mean()my_sharpe_avg = my_sharpe_ratios.mean()​print("Harold's sharpe ratio average:", harold_sharpe_avg)print("My sharpe ratio average:", my_sharpe_avg)

Harold's sharpe ratio average: -3.4928380351166615
My sharpe ratio average: -0.27781543864562924

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 # Use comparison operator to determine if Harold's sharpe ratio avg is greaterharold_sharpe_avg > my_sharpe_avg

False

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x # Import libraries and dependenciesimport osfrom pathlib import Pathimport numpy as npimport pandas as pdimport seaborn as sns%matplotlib inline​import matplotlib.pyplot as pltimport plotly.express as pxfrom MCForecastTools import MCSimulation

/opt/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm

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 # Set the file pathsbk_data = Path("../Resources/bk_data.csv")fang_data = Path("../Resources/fang_data.csv")jnj_data = Path("../Resources/jnj_data.csv")luv_data = Path("../Resources/luv_data.csv")mu_data = Path("../Resources/mu_data.csv")nke_data = Path("../Resources/nke_data.csv")sbux_data = Path("../Resources/sbux_data.csv")t_data = Path("../Resources/t_data.csv")wdc_data = Path("../Resources/wdc_data.csv")wrk_data = Path("../Resources/wrk_data.csv")​# Read the CSVs and set the `date` column as a datetime index to the DataFramebk_df = pd.read_csv(bk_data, index_col="date", infer_datetime_format=True, parse_dates=True)fang_df = pd.read_csv(fang_data, index_col="date", infer_datetime_format=True, parse_dates=True)jnj_df = pd.read_csv(jnj_data, index_col="date", infer_datetime_format=True, parse_dates=True)luv_df = pd.read_csv(luv_data, index_col="date", infer_datetime_format=True, parse_dates=True)mu_df = pd.read_csv(mu_data, index_col="date", infer_datetime_format=True, parse_dates=True)nke_df = pd.read_csv(nke_data, index_col="date", infer_datetime_format=True, parse_dates=True)sbux_df = pd.read_csv(sbux_data, index_col="date", infer_datetime_format=True, parse_dates=True)t_df = pd.read_csv(t_data, index_col="date", infer_datetime_format=True, parse_dates=True)wdc_df = pd.read_csv(wdc_data, index_col="date", infer_datetime_format=True, parse_dates=True)wrk_df = pd.read_csv(wrk_data, index_col="date", infer_datetime_format=True, parse_dates=True)​# Display a few rowswrk_df.head()​

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 # Create a new pivot table where the columns are the closing prices for each tickercombined_df = pd.concat([bk_df, fang_df, jnj_df, luv_df, mu_df, nke_df, sbux_df, t_df, wdc_df, wrk_df], axis="columns", join="inner")​# Sort datetime index in ascending order (past to present)combined_df.sort_index(inplace=True)​# Set column namescombined_df.columns = ['BK', 'FANG', 'JNJ', 'LUV', 'MU', 'NKE', 'SBUX', 'T', 'WDC', 'WRK']​# Display a few rowscombined_df.head()combined_df.columns = ['BK', 'FANG', 'JNJ', 'LUV', 'MU', 'NKE', 'SBUX', 'T', 'WDC', 'WRK']

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 # Use the `pct_change` function to calculate daily returnsdaily_returns = combined_df.pct_change()daily_returns.head()

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 # Use the `std` function and multiply by the square root of the number of trading days in a year to get annualized volatilityvolatility = daily_returns.std() * np.sqrt(252)volatility.sort_values(inplace=True)volatility

JNJ     0.161128
T       0.187579
SBUX    0.206805
BK      0.224967
NKE     0.246045
LUV     0.280021
WRK     0.303601
FANG    0.370704
WDC     0.415605
MU      0.479955
dtype: float64

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 # Drop the 5 stocks with the highest volatility in daily returnsdaily_returns.drop(columns=['MU', 'WDC', 'FANG', 'WRK', 'LUV'], inplace=True)daily_returns.head()

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 # Set weights for corresponding risk profile of stocks, use the `dot` function to sum the product each weight and the corresponding stock daily return# BK, LUV, NKE, SBUX, Tweights = [0.15, 0.05, 0.10, 0.2, 0.50]portfolio_returns = daily_returns.dot(weights)portfolio_returns.head()

date
2015-06-24         NaN
2015-06-25    0.004358
2015-06-26    0.006190
2015-06-29   -0.015759
2015-06-30   -0.002393
dtype: float64

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 # Use the `cumprod` function to calculate cumulative returnscumulative_returns = (1 + portfolio_returns).cumprod()cumulative_returns.head()

date
2015-06-24         NaN
2015-06-25    1.004358
2015-06-26    1.010575
2015-06-29    0.994650
2015-06-30    0.992269
dtype: float64

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 # Plot the returns of the portfolio in terms of moneyinitial_investment = 10000cumulative_profit = initial_investment * cumulative_returnscumulative_profit.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a263039d0>

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 # Re-calculate daily returns as the DataFrame was modified in part 1daily_returns = combined_df.pct_change()daily_returns.head()

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 # Use the `corr` function to calculate the correlation between stock returnscorrelation = daily_returns.corr()correlation

 # Use the `heatmap` function from the Seaborn library to visualize the correlation tablesns.heatmap(correlation, vmin=-1, vmax=1)

<matplotlib.axes._subplots.AxesSubplot at 0x1a2655c710>

 # Alternatively, sum total correlation for each stockcorrelation.sum(axis=0).sort_values()

T       3.123182
FANG    3.145768
LUV     3.494862
NKE     3.512710
JNJ     3.518928
MU      3.700211
SBUX    3.722712
WDC     3.814908
WRK     4.017159
BK      4.242129
dtype: float64

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 # Drop highly correlated stocks and keep only the non-correlated stocksnoncorrelated_daily_returns = daily_returns.drop(columns=['WRK', 'BK'])noncorrelated_daily_returns.head()

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 # Use the `mean` and `std` functions to calculate the annualized sharpe ratiosharpe_ratios = (noncorrelated_daily_returns.mean() * 252) / (noncorrelated_daily_returns.std() * np.sqrt(252))sharpe_ratiossharpe_ratios

FANG    0.421266
JNJ     0.609743
LUV     0.520850
MU      0.435027
NKE     0.585923
SBUX    0.545402
T      -0.050216
WDC    -0.245731
dtype: float64

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 # Drop the lowest sharpe ratio stocks and keep only the postive sharpe ratio stockscandidate_daily_returns = noncorrelated_daily_returns.drop(columns=['T', 'WDC', 'FANG'])candidate_daily_returns.head()

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 # Set Portfolio Weights, Calculate Daily and Cumulative Portfolio Returns, and Plot $10,000 Investment Over Time for Optimized Portfolioinitial_investment = 10000weights = [.2, .2, .2, .2, .2]candidate_portfolio_returns = candidate_daily_returns.dot(weights)candidate_cumulative_returns = (1 + candidate_portfolio_returns).cumprod()candidate_cumulative_profits = (initial_investment * candidate_cumulative_returns)candidate_cumulative_profits.plot(figsize=(20,10))

<matplotlib.axes._subplots.AxesSubplot at 0x1a27043d90>

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 # Set Portfolio Weights, Calculate Daily and Cumulative Portfolio Returns, and Plot $10,000 Investment Over Time for Uncorrelated Portfolioinitial_investment = 10000weights = [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125]noncorrelated_portfolio_returns = noncorrelated_daily_returns.dot(weights)noncorrelated_cumulative_returns = (1 + noncorrelated_portfolio_returns).cumprod()noncorrelated_cumulative_profits = (initial_investment * noncorrelated_cumulative_returns)noncorrelated_cumulative_profits.plot(figsize=(20,10))

<matplotlib.axes._subplots.AxesSubplot at 0x1a274cd490>

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 # Set Portfolio Weights, Calculate Daily and Cumulative Portfolio Returns, and Plot $10,000 Investment Over Time for Unoptimized Portfolioinitial_investment = 10000weights = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]unoptimized_portfolio_returns = daily_returns.dot(weights)unoptimized_cumulative_returns = (1 + unoptimized_portfolio_returns).cumprod()unoptimized_cumulative_profits = (initial_investment * unoptimized_cumulative_returns)unoptimized_cumulative_profits.plot(figsize=(20,10))# Set Portfolio Weights, Calculate Daily and Cumulative Portfolio Returns, and Plot $10,000 Investment Over Time for Unoptimized Portfolio

<matplotlib.axes._subplots.AxesSubplot at 0x1a278571d0>

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 # Set the figure of the chart and plot each data trend on the single figureax = candidate_cumulative_profits.plot(figsize=(20,10))noncorrelated_cumulative_profits.plot(ax=ax)unoptimized_cumulative_profits.plot(ax=ax)cumulative_profit.plot(ax=ax)​ax.legend(['PART 2: Diversified and Return-to-Risk Optimized Portfolio $10,000',            'PART 2: Diversified Portfolio $10,000',            'PART 2: Original/Unoptimized Portfolio $10,000',            'PART 1: Risk-Optimized Portfolio $10,000'])

<matplotlib.legend.Legend at 0x1a27e8e950>

​

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 # Import libraries and dependenciesimport numpy as npimport pandas as pdfrom pathlib import Path%matplotlib inline

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 # Set the file pathsbk_data = Path("../Resources/bk_data.csv")fang_data = Path("../Resources/fang_data.csv")jnj_data = Path("../Resources/jnj_data.csv")luv_data = Path("../Resources/luv_data.csv")mu_data = Path("../Resources/mu_data.csv")nke_data = Path("../Resources/nke_data.csv")sbux_data = Path("../Resources/sbux_data.csv")t_data = Path("../Resources/t_data.csv")wdc_data = Path("../Resources/wdc_data.csv")wrk_data = Path("../Resources/wrk_data.csv")​# Read the CSVs and set the `date` column as a datetime index to the DataFramebk_df = pd.read_csv(bk_data, index_col="date", infer_datetime_format=True, parse_dates=True)fang_df = pd.read_csv(fang_data, index_col="date", infer_datetime_format=True, parse_dates=True)jnj_df = pd.read_csv(jnj_data, index_col="date", infer_datetime_format=True, parse_dates=True)luv_df = pd.read_csv(luv_data, index_col="date", infer_datetime_format=True, parse_dates=True)mu_df = pd.read_csv(mu_data, index_col="date", infer_datetime_format=True, parse_dates=True)nke_df = pd.read_csv(nke_data, index_col="date", infer_datetime_format=True, parse_dates=True)sbux_df = pd.read_csv(sbux_data, index_col="date", infer_datetime_format=True, parse_dates=True)t_df = pd.read_csv(t_data, index_col="date", infer_datetime_format=True, parse_dates=True)wdc_df = pd.read_csv(wdc_data, index_col="date", infer_datetime_format=True, parse_dates=True)wrk_df = pd.read_csv(wrk_data, index_col="date", infer_datetime_format=True, parse_dates=True)​# Display a few rowswrk_df.head()

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 # Create a new pivot table where the columns are the closing prices for each tickercombined_df = pd.concat([bk_df, fang_df, jnj_df, luv_df, mu_df, nke_df, sbux_df, t_df, wdc_df, wrk_df], axis="columns", join="inner")​# Sort datetime index in ascending order (past to present)combined_df.sort_index(inplace=True)​# Set column namescombined_df.columns = ['BK', 'FANG', 'JNJ', 'LUV', 'MU', 'NKE', 'SBUX', 'T', 'WDC', 'WRK']​# Display a few rowscombined_df.head()

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 # Use the `pct_change` function to calculate daily returnsdaily_returns = combined_df.pct_change()daily_returns.head()

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 # Use the `std` function and multiply by the square root of the number of trading days in a year to get annualized volatilityvolatility = daily_returns.std() * np.sqrt(252)volatility.sort_values(inplace=True)volatility

JNJ     0.161208
T       0.187571
SBUX    0.206388
BK      0.225078
NKE     0.245942
LUV     0.280153
WRK     0.303048
FANG    0.370971
WDC     0.414714
MU      0.479754
dtype: float64

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 # Drop the five stocks with the highest volatility in daily returnsdaily_returns.drop(columns=['MU', 'WDC', 'JNJ', 'FANG', 'WRK'], inplace=True)daily_returns.head()

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 # Set weights for corresponding risk profile of stocks, use the `dot` function to multiply each weight by the corresponding stock daily return# BK, LUV, NKE, SBUX, Tweights = [0.15, 0.05, 0.10, 0.2, 0.50]portfolio_returns = daily_returns.dot(weights)portfolio_returns.head()

date
2015-06-24         NaN
2015-06-25    0.004668
2015-06-26    0.005681
2015-06-29   -0.016436
2015-06-30   -0.002416
dtype: float64

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 # Use the `cumprod` function to calculate cumulative returnscumulative_returns = (1 + portfolio_returns).cumprod()cumulative_returns.head()

date
2015-06-24         NaN
2015-06-25    1.004668
2015-06-26    1.010376
2015-06-29    0.993769
2015-06-30    0.991368
dtype: float64

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 # Plot the returns of the portfolio in terms of moneyinitial_investment = 10000cumulative_profit = initial_investment * cumulative_returnscumulative_profit.plot()

<AxesSubplot:xlabel='date'>

	TSLA
	open	high	low	close	volume
2017-05-04 00:00:00-04:00	307.435	307.77	290.7601	295.36	11653966
2017-05-05 00:00:00-04:00	298.000	308.55	296.8000	308.35	6695227
2017-05-08 00:00:00-04:00	310.600	313.79	305.8200	307.20	5969091
2017-05-09 00:00:00-04:00	309.380	321.99	309.1000	321.27	8158739
2017-05-10 00:00:00-04:00	321.560	325.40	318.1200	325.22	4754047

	0	1	2	3	4	5	6	7	8	9	...	990	991	992	993	994	995	996	997	998	999
0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	0.981222	0.990782	1.001497	0.968636	1.025662	0.985579	0.979159	0.959626	0.915200	1.100719	...	1.035320	0.974333	1.129787	0.991942	1.026417	1.070166	1.043902	1.019487	0.967719	0.982966
2	0.979845	0.932936	0.950812	1.001255	0.978447	0.956805	0.971956	0.959406	0.916128	1.014008	...	1.066326	0.974253	1.142736	1.009477	1.010455	1.083022	1.034498	1.023799	0.886175	0.955799
3	0.982248	0.958489	0.907635	1.076008	0.924043	0.941103	0.972826	0.994823	0.953968	1.040536	...	1.099464	0.990134	1.172254	0.948681	0.967827	1.055193	1.048936	1.023842	0.954594	0.966601
4	1.007947	0.994389	0.863432	1.100683	0.928010	0.884032	0.965998	0.947342	0.920592	1.079854	...	1.135327	0.972608	1.139030	0.909896	0.982004	1.046113	0.950495	0.958808	0.951463	0.908549
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
752	2.964285	6.281219	3.528422	5.569597	1.537283	1.927135	1.835281	2.273169	4.462258	6.555616	...	2.991133	3.373659	3.035196	1.345995	0.360124	34.367965	1.629844	8.824132	3.348889	10.723148
753	2.898127	5.929781	3.630850	5.614963	1.532372	1.913588	1.905665	2.160138	4.445566	6.939099	...	3.156057	3.510643	3.030412	1.298904	0.357633	34.687223	1.666808	9.236994	3.099606	10.409932
754	2.805524	5.824232	3.821479	6.019107	1.466395	1.944477	1.955238	2.174833	4.450806	6.638072	...	3.260135	3.581977	2.908168	1.199393	0.386841	36.692925	1.657787	9.105443	3.114519	10.542502
755	2.719517	5.297904	3.993555	6.214585	1.463815	1.966690	1.964821	2.180150	4.592842	6.726524	...	3.448536	3.769620	3.132997	1.138237	0.379576	37.370551	1.649728	9.167870	3.188418	10.650525
756	2.812057	5.466324	3.981998	6.201528	1.494064	2.016463	1.833008	2.172569	4.570772	6.460053	...	3.498799	3.943865	3.263377	1.214728	0.371717	37.689524	1.629001	9.286477	3.326631	10.344296

	mean	median	min	max
0	1.000000	1.000000	1.000000	1.000000
1	1.002953	1.002788	0.856863	1.129787
2	1.005907	1.005676	0.826181	1.203377
3	1.007616	1.006666	0.801393	1.232933
4	1.008332	1.004927	0.768805	1.294849

	mean	median	min	max
0	10000.000000	10000.000000	10000.000000	10000.000000
1	10029.532050	10027.879860	8568.632158	11297.874167
2	10059.066285	10056.758964	8261.814625	12033.766254
3	10076.159026	10066.656818	8013.931073	12329.325621
4	10083.317080	10049.269047	7688.053281	12948.490424

	Country	Happiness.Rank	Happiness.Score	Whisker.high	Whisker.low	Economy..GDP.per.Capita.	Family	Health..Life.Expectancy.	Freedom	Generosity	Trust..Government.Corruption.	Dystopia.Residual
0	Norway	1	7.537	7.594445	7.479556	1.616463	1.533524	0.796667	0.635423	0.362012	0.315964	2.277027
1	Denmark	2	7.522	7.581728	7.462272	1.482383	1.551122	0.792566	0.626007	0.355280	0.400770	2.313707
2	Iceland	3	7.504	7.622030	7.385970	1.480633	1.610574	0.833552	0.627163	0.475540	0.153527	2.322715
3	Switzerland	4	7.494	7.561772	7.426227	1.564980	1.516912	0.858131	0.620071	0.290549	0.367007	2.276716
4	Finland	5	7.469	7.527542	7.410458	1.443572	1.540247	0.809158	0.617951	0.245483	0.382612	2.430182
5	Netherlands	6	7.377	7.427426	7.326574	1.503945	1.428939	0.810696	0.585384	0.470490	0.282662	2.294804
6	Canada	7	7.316	7.384403	7.247597	1.479204	1.481349	0.834558	0.611101	0.435540	0.287372	2.187264
7	New Zealand	8	7.314	7.379510	7.248490	1.405706	1.548195	0.816760	0.614062	0.500005	0.382817	2.046456
8	Sweden	9	7.284	7.344095	7.223905	1.494387	1.478162	0.830875	0.612924	0.385399	0.384399	2.097538
9	Australia	10	7.284	7.356651	7.211349	1.484415	1.510042	0.843887	0.601607	0.477699	0.301184	2.065211
10	Israel	11	7.213	7.279853	7.146146	1.375382	1.376290	0.838404	0.405989	0.330083	0.085242	2.801757
11	Costa Rica	12	7.079	7.168112	6.989888	1.109706	1.416404	0.759509	0.580132	0.214613	0.100107	2.898639
12	Austria	13	7.006	7.070670	6.941330	1.487097	1.459945	0.815328	0.567766	0.316472	0.221060	2.138506
13	United States	14	6.993	7.074657	6.911343	1.546259	1.419921	0.774287	0.505741	0.392579	0.135639	2.218113
14	Ireland	15	6.977	7.043352	6.910649	1.535707	1.558231	0.809783	0.573110	0.427858	0.298388	1.773869
15	Germany	16	6.951	7.005382	6.896619	1.487923	1.472520	0.798951	0.562511	0.336269	0.276732	2.015770
16	Belgium	17	6.891	6.955821	6.826179	1.463781	1.462313	0.818092	0.539771	0.231503	0.251343	2.124210
17	Luxembourg	18	6.863	6.923686	6.802314	1.741944	1.457584	0.845089	0.596628	0.283181	0.318834	1.619512
18	United Kingdom	19	6.714	6.783792	6.644209	1.441634	1.496460	0.805336	0.508190	0.492774	0.265428	1.704144
19	Chile	20	6.652	6.739251	6.564749	1.252785	1.284025	0.819480	0.376895	0.326662	0.082288	2.509586
20	United Arab Emirates	21	6.648	6.722047	6.573952	1.626343	1.266410	0.726798	0.608345	0.360942	0.324490	1.734704
21	Brazil	22	6.635	6.725470	6.544531	1.107353	1.431306	0.616552	0.437454	0.162350	0.111093	2.769267
22	Czech Republic	23	6.609	6.683862	6.534138	1.352682	1.433885	0.754444	0.490946	0.088107	0.036873	2.451862
23	Argentina	24	6.599	6.690085	6.507915	1.185295	1.440451	0.695137	0.494519	0.109457	0.059740	2.614005
24	Mexico	25	6.578	6.671149	6.484851	1.153184	1.210862	0.709979	0.412730	0.120990	0.132774	2.837155
25	Singapore	26	6.572	6.636723	6.507277	1.692278	1.353814	0.949492	0.549841	0.345966	0.464308	1.216362
26	Malta	27	6.527	6.598397	6.455603	1.343280	1.488412	0.821944	0.588767	0.574731	0.153066	1.556863
27	Uruguay	28	6.454	6.545906	6.362094	1.217560	1.412228	0.719217	0.579392	0.175097	0.178062	2.172410
28	Guatemala	29	6.454	6.566874	6.341126	0.872002	1.255585	0.540240	0.531311	0.283488	0.077223	2.893891
29	Panama	30	6.452	6.557131	6.346870	1.233748	1.373193	0.706156	0.550027	0.210557	0.070984	2.307200
...	...	...	...	...	...	...	...	...	...	...	...	...
125	Congo (Kinshasa)	126	4.280	4.357811	4.202190	0.092102	1.229023	0.191407	0.235961	0.246456	0.060241	2.224959
126	Mali	127	4.190	4.269671	4.110329	0.476180	1.281473	0.169366	0.306614	0.183354	0.104970	1.668191
127	Ivory Coast	128	4.180	4.275183	4.084817	0.603049	0.904780	0.048642	0.447706	0.201237	0.130062	1.844964
128	Cambodia	129	4.168	4.278518	4.057483	0.601765	1.006238	0.429783	0.633376	0.385923	0.068106	1.042941
129	Sudan	130	4.139	4.345747	3.932253	0.659517	1.214009	0.290921	0.014996	0.182317	0.089848	1.687066
130	Ghana	131	4.120	4.222707	4.017293	0.667225	0.873665	0.295638	0.423026	0.256924	0.025336	1.577868
131	Ukraine	132	4.096	4.185410	4.006590	0.894652	1.394538	0.575904	0.122975	0.270061	0.023029	0.814382
132	Uganda	133	4.081	4.195800	3.966200	0.381431	1.129828	0.217633	0.443186	0.325766	0.057070	1.526363
133	Burkina Faso	134	4.032	4.124059	3.939941	0.350228	1.043280	0.215844	0.324368	0.250865	0.120328	1.727213
134	Niger	135	4.028	4.111947	3.944053	0.161925	0.993025	0.268505	0.363659	0.228674	0.138573	1.873983
135	Malawi	136	3.970	4.077479	3.862521	0.233442	0.512569	0.315090	0.466915	0.287170	0.072712	2.081786
136	Chad	137	3.936	4.034712	3.837289	0.438013	0.953856	0.041135	0.162342	0.216114	0.053582	2.071238
137	Zimbabwe	138	3.875	3.978700	3.771300	0.375847	1.083096	0.196764	0.336384	0.189143	0.095375	1.597970
138	Lesotho	139	3.808	4.044344	3.571656	0.521021	1.190095	0.000000	0.390661	0.157497	0.119095	1.429835
139	Angola	140	3.795	3.951642	3.638358	0.858428	1.104412	0.049869	0.000000	0.097926	0.069720	1.614482
140	Afghanistan	141	3.794	3.873661	3.714338	0.401477	0.581543	0.180747	0.106180	0.311871	0.061158	2.150801
141	Botswana	142	3.766	3.874123	3.657877	1.122094	1.221555	0.341756	0.505196	0.099348	0.098583	0.377914
142	Benin	143	3.657	3.745784	3.568217	0.431085	0.435300	0.209930	0.425963	0.207948	0.060929	1.885631
143	Madagascar	144	3.644	3.714319	3.573681	0.305809	0.913020	0.375223	0.189197	0.208733	0.067232	1.584613
144	Haiti	145	3.603	3.734715	3.471285	0.368610	0.640450	0.277321	0.030370	0.489204	0.099872	1.697168
145	Yemen	146	3.593	3.692750	3.493250	0.591683	0.935382	0.310081	0.249464	0.104125	0.056767	1.345601
146	South Sudan	147	3.591	3.725539	3.456462	0.397249	0.601323	0.163486	0.147062	0.285671	0.116794	1.879567
147	Liberia	148	3.533	3.653756	3.412244	0.119042	0.872118	0.229918	0.332881	0.266550	0.038948	1.673286
148	Guinea	149	3.507	3.584428	3.429572	0.244550	0.791245	0.194129	0.348588	0.264815	0.110938	1.552312
149	Togo	150	3.495	3.594038	3.395962	0.305445	0.431883	0.247106	0.380426	0.196896	0.095665	1.837229
150	Rwanda	151	3.471	3.543030	3.398970	0.368746	0.945707	0.326425	0.581844	0.252756	0.455220	0.540061
151	Syria	152	3.462	3.663669	3.260331	0.777153	0.396103	0.500533	0.081539	0.493664	0.151347	1.061574
152	Tanzania	153	3.349	3.461430	3.236570	0.511136	1.041990	0.364509	0.390018	0.354256	0.066035	0.621130
153	Burundi	154	2.905	3.074690	2.735310	0.091623	0.629794	0.151611	0.059901	0.204435	0.084148	1.683024
154	Central African Republic	155	2.693	2.864884	2.521116	0.000000	0.000000	0.018773	0.270842	0.280876	0.056565	2.066005

	Happiness.Rank	Happiness.Score	Whisker.high	Whisker.low	Economy..GDP.per.Capita.	Family	Health..Life.Expectancy.	Freedom	Generosity	Trust..Government.Corruption.	Dystopia.Residual
Country
Kuwait	39	6.105	6.191957	6.018043	1.632952	1.259699	0.632106	0.496338	0.228290	0.215160	1.640425
Luxembourg	18	6.863	6.923686	6.802314	1.741944	1.457584	0.845089	0.596628	0.283181	0.318834	1.619512
Norway	1	7.537	7.594445	7.479556	1.616463	1.533524	0.796667	0.635423	0.362012	0.315964	2.277027
Qatar	35	6.375	6.568477	6.181523	1.870766	1.274297	0.710098	0.604131	0.330474	0.439299	1.145464
Singapore	26	6.572	6.636723	6.507277	1.692278	1.353814	0.949492	0.549841	0.345966	0.464308	1.216362

	Happiness.Rank	Happiness.Score	Whisker.high	Whisker.low	Economy..GDP.per.Capita.	Family	Health..Life.Expectancy.	Freedom	Generosity	Trust..Government.Corruption.	Dystopia.Residual
Country
Belize	50	5.956	6.197242	5.714757	0.907975	1.081418	0.450192	0.547509	0.240016	0.096581	2.631956
Bhutan	97	5.011	5.079335	4.942666	0.885416	1.340127	0.495879	0.501538	0.474055	0.173380	1.140184
Bolivia	58	5.823	5.903977	5.742023	0.833757	1.227619	0.473630	0.558733	0.225561	0.060478	2.443279
Bosnia and Herzegovina	90	5.182	5.276336	5.087665	0.982409	1.069336	0.705186	0.204403	0.328867	0.000000	1.892173
El Salvador	45	6.003	6.108635	5.897364	0.909784	1.182125	0.596019	0.432453	0.078258	0.089981	2.714594

	Total Active Loans	Total Average Loan Amount	2015 - 2016	2010 - 2014	Self Help Loan	Leveraged Loan
State Code
AK	897	148811.39	189583.49	172371.39	96	450
AL	7455	435054.53	686066.79	648663.44	18	605
AR	6186	263212.27	355445.70	351562.42	861	567
AZ	3982	597591.94	507532.91	599688.64	1718	1057
CA	8875	2805653.85	3008866.40	2881741.23	4134	2231

	filing_date	municipality	amount
0	2009	Aleppo Township	5
1	2009	Aspinwall Boro	4
2	2009	Avalon Boro	22
3	2009	Baldwin Boro	47
4	2009	Baldwin Township	11

	Close
Date
2019-05-13	1132.03
2019-05-10	1164.27
2019-05-09	1162.38
2019-05-08	1166.27
2019-05-07	1174.10

	BTC	BTT	DOGE	ETH	LTC	XRP	SOLVE	XLM	TRON	ZIL
Date
2019-05-20	8118.39	0.001187	0.003041	261.09	95.06	0.398003	0.338667	0.135545	0.028509	0.017992
2019-05-19	8200.00	0.001056	0.003178	234.03	86.65	0.417700	0.300430	0.141485	0.029277	0.019170
2019-05-18	7262.40	0.000993	0.003005	244.59	89.88	0.372736	0.287630	0.128833	0.026823	0.017685
2019-05-17	7363.69	0.000957	0.002962	264.39	95.75	0.386193	0.259506	0.126562	0.027220	0.017892
2019-05-16	7878.96	0.000885	0.003168	249.00	102.26	0.419707	0.223316	0.137283	0.028857	0.018725

	BTC	BTT	DOGE	ETH	LTC	XRP	SOLVE	XLM	TRON	ZIL
Date
2019-05-19	0.010052	-0.110362	0.045051	-0.103642	-0.088470	0.049490	-0.112904	0.043823	0.026939	0.065474
2019-05-18	-0.114341	-0.059659	-0.054437	0.045122	0.037276	-0.107647	-0.042606	-0.089423	-0.083820	-0.077465
2019-05-17	0.013947	-0.036254	-0.014309	0.080952	0.065309	0.036103	-0.097778	-0.017627	0.014801	0.011705
2019-05-16	0.069974	-0.075235	0.069548	-0.058209	0.067990	0.086780	-0.139457	0.084709	0.060140	0.046557
2019-05-15	0.041168	-0.037288	0.044192	-0.126747	-0.103364	0.086484	0.007420	-0.002047	0.086322	0.088224

	close
date
2019-05-20	35.15
2019-05-17	36.66
2019-05-16	37.42
2019-05-15	37.44
2019-05-14	37.19

	BK	FANG	JNJ	LUV	MU	NKE	SBUX	T	WDC	WRK
date
2015-06-24	43.29	77.96	99.33	34.35	24.06	53.110	53.710	35.78	87.73	58.5699
2015-06-25	42.85	76.39	99.12	34.49	24.02	52.610	54.070	36.18	85.95	57.2022
2015-06-26	42.98	75.25	99.64	34.32	19.66	54.855	54.620	36.12	84.45	57.2022
2015-06-29	41.79	75.16	97.68	33.18	18.73	53.835	53.550	35.77	81.96	56.1857
2015-06-30	41.97	75.38	97.46	33.09	18.84	54.010	53.615	35.52	78.42	56.2781

	BK	FANG	JNJ	LUV	MU	NKE	SBUX	T	WDC	WRK
BK	1.000000	0.316856	0.350064	0.390731	0.339511	0.352312	0.361186	0.284662	0.373527	0.473280
FANG	0.316856	1.000000	0.221352	0.169883	0.257745	0.228170	0.211182	0.186524	0.259662	0.294395
JNJ	0.350064	0.221352	1.000000	0.248263	0.173480	0.297197	0.366453	0.343575	0.223365	0.295180
LUV	0.390731	0.169883	0.248263	1.000000	0.296751	0.285755	0.314467	0.220850	0.273768	0.294395
MU	0.339511	0.257745	0.173480	0.296751	1.000000	0.196334	0.247012	0.174143	0.609577	0.405658
NKE	0.352312	0.228170	0.297197	0.285755	0.196334	1.000000	0.398614	0.240217	0.242891	0.271221
SBUX	0.361186	0.211182	0.366453	0.314467	0.247012	0.398614	1.000000	0.238597	0.281220	0.303981
T	0.284662	0.186524	0.343575	0.220850	0.174143	0.240217	0.238597	1.000000	0.153232	0.281383
WDC	0.373527	0.259662	0.223365	0.273768	0.609577	0.242891	0.281220	0.153232	1.000000	0.397666
WRK	0.473280	0.294395	0.295180	0.294395	0.405658	0.271221	0.303981	0.281383	0.397666	1.000000

	KO					MSFT
	open	high	low	close	volume	open	high	low	close	volume
2017-05-01 00:00:00-04:00	43.15	43.500	43.10	43.230	11470244	68.67	69.55	68.500	69.43	27119635
2017-05-02 00:00:00-04:00	43.22	43.440	43.16	43.380	9733052	69.71	69.71	69.130	69.30	19179268
2017-05-03 00:00:00-04:00	43.33	43.485	43.15	43.315	8712620	69.38	69.38	68.710	69.08	24576064
2017-05-04 00:00:00-04:00	43.42	43.810	43.40	43.660	8638866	69.03	69.08	68.640	68.81	18561598
2017-05-05 00:00:00-04:00	43.79	43.840	43.56	43.680	6678133	68.90	69.03	68.485	68.99	15644971

	KO						MSFT
	open	high	low	close	volume	daily_return	open	high	low	close	volume	daily_return
2017-05-01 00:00:00-04:00	43.15	43.500	43.10	43.230	11470244	NaN	68.67	69.55	68.500	69.43	27119635	NaN
2017-05-02 00:00:00-04:00	43.22	43.440	43.16	43.380	9733052	0.003470	69.71	69.71	69.130	69.30	19179268	-0.001872
2017-05-03 00:00:00-04:00	43.33	43.485	43.15	43.315	8712620	-0.001498	69.38	69.38	68.710	69.08	24576064	-0.003175
2017-05-04 00:00:00-04:00	43.42	43.810	43.40	43.660	8638866	0.007965	69.03	69.08	68.640	68.81	18561598	-0.003909
2017-05-05 00:00:00-04:00	43.79	43.840	43.56	43.680	6678133	0.000458	68.90	69.03	68.485	68.99	15644971	0.002616

	0	1	2	3	4	5	6	7	8	9	...	490	491	492	493	494	495	496	497	498	499
0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	1.011183	1.003640	0.995387	1.003003	1.002283	0.991261	0.993851	1.004959	0.995040	1.005974	...	0.987744	0.999161	1.001092	1.015067	0.991717	0.993505	1.005455	0.992440	1.014116	0.996327
2	1.010516	1.013914	0.986976	0.994755	0.988325	0.997444	0.982326	1.005981	1.001946	1.013416	...	0.988858	1.002668	1.005572	1.002438	1.004596	0.979046	1.003859	1.007666	1.025195	0.991248
3	1.020538	1.014956	0.995326	0.971891	1.002391	1.008548	0.998577	1.009823	1.023618	1.006224	...	1.007736	1.009974	1.001745	1.003470	1.013095	0.996308	1.013724	1.001729	1.010593	0.994025
4	1.008357	1.015980	0.990913	0.967137	0.995449	1.011326	1.007435	1.013989	1.032095	0.995030	...	0.998612	1.015970	0.978090	1.026153	1.020178	1.001752	1.022909	0.996071	1.014151	0.999556
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1256	2.320943	3.625409	3.503447	1.085580	1.246446	1.439788	1.434845	2.251037	2.848876	1.858250	...	3.062218	2.144138	1.879846	2.887162	1.406283	4.133657	1.443959	1.986593	1.575785	4.102522
1257	2.298058	3.670266	3.450661	1.093163	1.257378	1.437472	1.410902	2.249188	2.823875	1.860901	...	3.094995	2.138825	1.869621	2.874659	1.434135	4.148609	1.428483	1.990941	1.552572	4.075057
1258	2.333132	3.619737	3.421655	1.104514	1.269939	1.423639	1.428474	2.263686	2.866994	1.865590	...	3.090344	2.174377	1.872586	2.897630	1.441873	4.162761	1.412225	1.969911	1.548777	4.108532
1259	2.314822	3.675878	3.392552	1.120610	1.267853	1.433143	1.444003	2.287083	2.925508	1.881977	...	3.114467	2.166647	1.836733	2.940865	1.454774	4.256929	1.408203	1.939488	1.550266	4.120576
1260	2.331860	3.727082	3.401183	1.110582	1.250750	1.448851	1.432627	2.318826	2.954205	1.878881	...	3.066632	2.141222	1.838587	2.956516	1.449248	4.273349	1.436359	1.958227	1.557093	4.071631

		Close
Date	Date
2019	2	1119.92
	3	1173.31
	4	1188.48
	5	1132.03

Student Activity: Financial Forecasting¶

Get 3 Years Worth of Data via API Call and Read in as DataFrame¶

Run the Monte Carlo Simulation¶

Plot the Simulated Daily Returns Trajectory for TSLA over the Next Year (252 Trading Days)¶

Calculate the Simulated Profits/Losses of $10,000 Investment in TSLA Over the Next Three Years¶

Plot the Simulated Profits/Losses of $10,000 Investment in TSLA Over the Next 252 Trading Days¶

Calculate the range of the possible outcomes of our $10,000 investments in TSLA stocks¶

World Happiness¶

1. Import Libraries and Dependencies¶

2. Set File Path to CSV using Pathlib¶

3. Import CSV into Pandas DataFrame¶

4. Set Index and Sort Index in Ascending Order¶

5. Select the Happiness Score for Vietnam¶

6. Select the Happiness Rank and Score for Belgium, Belize, Benin, Bhutan, and Bolivia¶

7. Select the Freedom and Government Trust for Every Country¶

8. Select all the Countries Where GDP is Greater than or Equal to 1.6¶

9. Select all the Countries Where the Happiness Score is Greater than or Equal to 5 and GDP is Less than or Equal to 1¶

Composing Masterpieces¶

Prep the data¶

Plot Total Average Loan Amount¶

Plot Total Average Loan Amount for 2015 - 2016 and 2010 - 2014¶

Compose plots for 2015 - 2016 and 2010 - 2014 using + operator¶

Compose plots for state averages, 2015 - 2016, and 2010 2014 using + operator¶

Compose plots for state averages, 2015 - 2016, and 2010 2014 using * operator¶

Plotting with Plotly¶

Plot Foreclosures¶

Plot relationship between municipality and the number of foreclosures¶

Plot the relationship between filing date and municipality¶

Plot the progression of foreclosures¶

Demo: Forcasting Stock Performance in Our Portfolio¶

Import Dependencies¶

Get Past 3 Year's Worth of Microsoft and Coca-Cola Stock Price Data via Alpaca API Call¶

The MCForecastTools Library at a Glance¶

Simulate five year portfolio growth using Monte Carlo simulation¶

Import Libraries and Dependencies¶

Read CSV as Pandas DataFrame¶

Assess & Clean Data¶

Group by year and month¶

Access Close for May 2019 Using Multi-indexing Lookup¶

Calculate mean Close Price of GOOG for 2019¶

Risky Business¶

Read in data¶

Calculate daily returns¶

Concat portfolios into one DataFrame¶

Calculate std dev for all data¶

Calculate sharpe ratio for all data¶

Plot Sharpe Ratios¶

How many smart investments did Harold make compared to risky investments? How many did you make?¶

Which cryptos have been the smartest investment?¶

Calculate annualized std dev for each portfolio individually¶

Calculate the sharpe ratios for each individual portfolio¶

Average sharpe ratios for each portfolio and output to screen¶

Compare Sharpe Ratios Averages¶

Which portfolio is the smartest investment, based off of risk to reward ratio?¶

Student Activity: Portfolio Planner Part 2¶

PART I: Portfolio Optimization via Risk Evaluation¶

Read CSVs as DataFrames¶

Combine DataFrames, Sort Index, and Rename Columns¶

Calculate Daily Returns¶

Evaluate Riskiness of Stocks¶

Drop High Volatility Stocks¶

Set Portfolio Allocations/Weights and Calculate Portfolio Daily Returns¶

Calculate Cumulative Returns¶

Plot Return of Portfolio Starting with Initial Investment of $10,000¶

PART 2: Portfolio Optimization via Correlation and Return-to-risk (Sharpe Ratio) Evaluations¶

Reset Daily Returns DataFrame¶

Evaluate Correlation of Stock Returns¶

Drop the Two Most Consistently Correlated Stocks¶

Evaluate Reward-to-Risk (Sharpe Ratio) of Minimally-Correlated Stocks¶

Drop the Three Lowest Sharpe Ratio Stocks¶

Evaluate Investment in Minimally-Correlated (Diversified) and Return-to-Risk (Sharpe Ratio) Optimized Portfolio¶

Evaluate Investment in Non-Correlated (Diversified) Portfolio¶

Evaluate Investment in Original/Unoptimized Portfolio (No Modifications)¶

Overlay Hypothetical Portfolio Investments¶

Student Activity: Portfolio Planner Part I¶

Read CSVs as DataFrames¶

Combine DataFrames, Sort Index, and Rename Columns¶

Calculate Daily Returns¶

Evaluate Riskiness of Stocks¶

Drop High Volatility Stocks¶

Plot the Simulated Daily Returns Trajectory for `TSLA` over the Next Year (252 Trading Days)¶

Calculate the Simulated Profits/Losses of $10,000 Investment in `TSLA` Over the Next Three Years¶

Plot the Simulated Profits/Losses of $10,000 Investment in `TSLA` Over the Next `252` Trading Days¶

Calculate the range of the possible outcomes of our $10,000 investments in `TSLA` stocks¶

Compose plots for 2015 - 2016 and 2010 - 2014 using `+` operator¶

Compose plots for state averages, 2015 - 2016, and 2010 2014 using `+` operator¶

Compose plots for state averages, 2015 - 2016, and 2010 2014 using `*` operator¶

Group by `year` and `month`¶

Access `Close` for May 2019 Using Multi-indexing Lookup¶

Calculate `mean` Close Price of GOOG for 2019¶

`Concat` portfolios into one DataFrame¶

Calculate `std dev` for all data¶

Calculate `sharpe ratio` for all data¶

Plot Return of Portfolio Starting with Initial Investment of `$10,000`¶

Plot Return of Portfolio Starting with Initial Investment of `$10,000`¶